Despite recent success in incorporating learning into point cloud registration, many works
focus on learning feature descriptors and continue to rely on nearest-neighbor feature …

We study the problem of extracting accurate correspondences for point cloud registration.
Recent keypoint-free methods bypass the detection of repeatable keypoints which is difficult …

PointNet has revolutionized how we think about representing point clouds. For classification
and segmentation tasks, the approach and its subsequent variants/extensions are …

We present DeepVCP-a novel end-to-end learning-based 3D point cloud registration
framework that achieves comparable registration accuracy to prior state-of-the-art geometric …

Deep point cloud registration methods face challenges to partial overlaps and rely on
labeled data. To address these issues, we propose UDPReg, an unsupervised deep …

Learning distinctive point-wise features is critical for low-overlap point cloud registration.
Recently, it has achieved huge success in incorporating Transformer into point cloud feature …

Point cloud registration is essential for many applications. However, existing real datasets
require extremely tedious and costly annotations, yet may not provide accurate camera …

Point cloud registration is a key problem for computer vision applied to robotics, medical
imaging, and other applications. This problem involves finding a rigid transformation from …

Although point cloud registration has achieved remarkable advances in object-level and
indoor scenes, large-scale registration methods are rarely explored. Challenges mainly …

An ideal point cloud registration framework should have superior accuracy, acceptable
efficiency, and strong generalizability. However, this is highly challenging since existing …